Projection Mapping Implementation: Enabling Direct Externalization of Perception Results and Action Intent to Improve Robot Explainability
This addresses the issue of robot explainability for human-robot interaction, though it is incremental as it builds on existing projection concepts with a new implementation.
The paper tackles the problem of robots' internal states like perception and intent being unclear to humans by implementing projection mapping to directly display these states onto the environment, providing a tool with documentation and code for researchers.
Existing research on non-verbal cues, e.g., eye gaze or arm movement, may not accurately present a robot's internal states such as perception results and action intent. Projecting the states directly onto a robot's operating environment has the advantages of being direct, accurate, and more salient, eliminating mental inference about the robot's intention. However, there is a lack of tools for projection mapping in robotics, compared to established motion planning libraries (e.g., MoveIt). In this paper, we detail the implementation of projection mapping to enable researchers and practitioners to push the boundaries for better interaction between robots and humans. We also provide practical documentation and code for a sample manipulation projection mapping on GitHub: https://github.com/uml-robotics/projection_mapping.